AI: Catch-22 of data management

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Simon Jelley, General Manager for SaaS Protection, Endpoint and Backup Exec at Veritas Technologies.

Exactly 160 years ago, novelist and critic Samuel Butler made famous that Darwinian evolution could apply to machines and they could one day become sentient. Nearly 75 years ago, legendary mathematician Alan Turing created his well-known Turing test to measure machine intelligence. twenty years ago, NASA’s Spirit and Opportunity rovers made their way across the surface of Mars and autonomously avoided obstacles using AI.

And now OpenAI has launched ChatGPT. With a simple online or mobile app chat box user interface available even to the tech novice among us, it is arguably the most advanced And accessible AI ever. Need an essay on quantum computing? Ask ChatGPT. Want to know how to arrange flowers for a bouquet? Ask ChatGPT. Want to know how to best connect with the youth of your church? Ask ChatGPT. (Yes, that last one comes from personal experience as a member of my church’s pastoral council. It did pretty well!)

ChatGPT, along with contemporaries such as Google Bard, has made an unprecedented buzz about AI. This reminded me that a little over a year ago I wrote an article that focused mainly on extolling the benefits of AI for autonomous data management.

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The challenges of AI

But there is another side to the story. Samuel Butler’s suggestion that Darwinian evolution might apply to machines eventually led him to the conclusion that machines could eventually supplant humanity. And more recently, some of the greatest minds of our time have advocated pausing AI development until society can implement a series of safety protocols.

AI is indeed a double-edged sword. And it’s the catch-22 of data management: Just as it has huge benefits, it can also potentially cause significant problems. For example:

• Cybercrime

It cannot be ruled out that AI chatbots like ChatGPT and the technology behind them are ushering in a golden age of cybercrime where even the least tech-savvy criminals steal data for profit. Need ransomware? Just ask. Or advanced cybercriminals may be able to use these tools to build even more innovative malware and attack vectors. Do you need a more sophisticated phishing scheme? Just ask.

• Volume, speed and variety of data

I also wrote earlier about the three R’s of data: volume, the total amount of data your company creates; speed, the speed at which your company creates that data; and variation, the number of formats data comes in. When you take away everything else, AI is really just absorbing existing data, “thinking” about that data, and then even more facts faster and in one greater variety of formats.

Thus, AI can also increase the challenge of managing the already massive amounts of data scattered across the on-premises and multi-cloud infrastructures faced by enterprises, which has both security and cost implications. And with the world’s data – which is stored in largely fossil fuel-powered data centers – already being generated as much carbon waste as the entire aviation industrythe potentially overwhelming amount of unnecessary data generated by AI can also have a significant impact on the environment.

• Data compliance and governance

In addition, AI tools such as ChatGPT pose data compliance and governance challenges. Employees turn to them to streamline their work. While this can improve productivity, there is a huge risk when employees share confidential information, such as regulatory documents, earnings reports, or personally identifiable information, with chatbots. The AI ​​can even use that information as part of its machine learning process to provide answers it gives to others.

Overcome some of AI’s challenges with the help of…AI

Make no mistake though: I am optimistic about the future of AI and the impact it can have on our lives, especially data management in an increasingly multi-cloud world. In fact, I believe AI-driven data management, doing what IT teams fail or don’t have the time to do, is the answer to many of the challenges posed by the wider adoption of AI. Here are just a few ways AI-powered data management can address some of the potential problems AI itself could cause.

• Dynamic cyber resilience

The threat landscape evolved at an alarming rate even before generative AI tools, but now it could accelerate to unimaginable levels. The solution may be to fight fire with fire. Applying AI-powered anomaly detection and other similar security measures as part of a comprehensive data management strategy can help companies better protect against the effects of the evolving threat landscape.

• Data services that provide, optimize and self-heal themselves

Data governance leaders must continue to develop services that can self-provision, self-heal, and self-optimize, including advanced autonomous deduplication. This may help explain the massive amounts of data in today’s multi-cloud environments, which is sure to only increase as generative AI increases the volume, speed and variety of data being created. As I said before, “In practice, this will be like autonomously providing data protection policies as new services and users are deployed, and autonomously monitoring and rolling out new policies that match the perceived use of a company’s data – again , all with no human decision-making required.”

• Standardized regulations

AI is already playing a role in cutting-edge data compliance and governance solutions, such as staying ahead of potential compliance issues across an unprecedented number of enterprise communications platforms, and could eventually play a role in helping prevent data leakage through generative AI tools. First, though, we must accept, advocate for, and implement legal restrictions to keep potentially sensitive information out of these tools. Until these regulations are in place, business leaders can join a growing list of companies implementing their own rules to avoid data compliance issues arising from employees using generative AI tools.

In summary, for all their benefits, generative AI tools also present unique challenges. But AI itself, along with adequate regulatory constraints, can help overcome those challenges.


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